Which adjustment will help a Structured Streaming job process records in less than 10 seconds during peak hours?

Which adjustment will help a Structured Streaming job process records in less than 10 seconds during peak hours?

• A. Increase the trigger interval to 30 seconds
• B. Decrease the trigger interval to 5 seconds
• C. Increase the number of executors
• D. Reduce the data volume during peak hours

Answer: (B)

Decreasing the trigger interval to 5 seconds in a Structured Streaming job allows the system to process data more frequently, which can help achieve quicker processing times for incoming records, especially during peak hours when you want to keep latency down. A smaller trigger interval means that the streaming job will attempt to process data every 5 seconds, rather than waiting longer periods. This adjustment is crucial when aiming for responsiveness in data processing, as it reduces the delay between when data arrives and when it is processed. While increasing the number of executors could help with throughput and performance by spreading the workload across more resources, it does not directly impact the latency of how quickly each batch of data is processed. Similarly, increasing the trigger interval to a longer duration would lead to increased latency, making it less optimal for achieving processing in less than 10 seconds. Reducing the data volume during peak hours may alleviate some pressure on processing, but it does not actively enhance the speed of record processing; it merely limits the workload. Thus, decreasing the trigger interval effectively aligns with the goal of minimizing processing time during busy periods.

Decreasing the trigger interval to 5 seconds in a Structured Streaming job allows the system to process data more frequently, which can help achieve quicker processing times for incoming records, especially during peak hours when you want to keep latency down. A smaller trigger interval means that the streaming job will attempt to process data every 5 seconds, rather than waiting longer periods. This adjustment is crucial when aiming for responsiveness in data processing, as it reduces the delay between when data arrives and when it is processed.

While increasing the number of executors could help with throughput and performance by spreading the workload across more resources, it does not directly impact the latency of how quickly each batch of data is processed. Similarly, increasing the trigger interval to a longer duration would lead to increased latency, making it less optimal for achieving processing in less than 10 seconds. Reducing the data volume during peak hours may alleviate some pressure on processing, but it does not actively enhance the speed of record processing; it merely limits the workload. Thus, decreasing the trigger interval effectively aligns with the goal of minimizing processing time during busy periods.